Title: Research on anomaly recognition of the English MOOC teaching platform based on deep feature learning

Authors: Haixia Yu

Addresses: School of General Education of Beijing Polytechnic, Beijing, 100176, China

Abstract: To improve the recognition rate and accuracy of the English MOOC teaching platform access exceptions, and reduce the recognition time, the anomaly recognition method of the English MOOC teaching platform based on deep feature learning is proposed. Firstly, the abnormal data accessed by English MOOC teaching platform under the influence of Gaussian white noise is analysed and the abnormal data is denoised. Then, according to the data denoising results, the features of abnormal data accessed by English MOOC teaching platform are extracted. Finally, using depth feature learning, unsupervised pre-training stack noise reduction self-encoder layer by layer obtains the depth features of abnormal data, and combined with softmax classifier to realise the recognition of abnormal access to English MOOC teaching platform. Experimental results show that the anomaly recognition rate and accuracy of the proposed method are 98.1% and 96.8% respectively, and the anomaly recognition time is only 4.7 s.

Keywords: deep feature learning; stack noise reduction self-encoder; English MOOC; softmax classifier; teaching platform; access exception identification.

DOI: 10.1504/IJBIDM.2024.137734

International Journal of Business Intelligence and Data Mining, 2024 Vol.24 No.3/4, pp.364 - 378

Received: 28 Nov 2022
Accepted: 07 Mar 2023
Published online: 04 Apr 2024 *

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